Identification of a prognostic risk-scoring model and risk signatures based on glycosylation-associated cluster in breast cancer.

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But there remains a strong need for more specific biomarkers and broader alternatives for personalized treatment. Our study classified breast cancer samples from The Cancer Genome Atlas (TCGA) into three groups based on glycosylation-associated genes and then identified differentially expressed genes under different glycosylation patterns to construct a prognostic model. The final prognostic model containing 23 key molecules achieved exciting performance both in the TCGA training set and testing set GSE42568 and GSE58812. The risk score also showed a significant difference in predicting overall clinical survival and immune infiltration analysis. This work helped us to understand the heterogeneity of breast cancer from another perspective and indicated that the identification of risk scores based on glycosylation patterns has potential clinical implications and immune-related value for breast cancer.", "claims": [{"id": "claim-method", "statement": "Then, we fitted a multivariate Cox proportional hazard model to develop more valuable integrated molecules in the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36338982", "locator": "sections[6] \"Construction and efficacy of risk-scoring model\" paragraph 2", "quote": "Then, we fitted a multivariate Cox proportional hazard model to develop more valuable integrated molecules in the training set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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